Short Birth Intervals, Maternal Care, and Neonatal Mortality in Rwanda: A Facility-Based Retrospective Cohort Study Using Hierarchical Modelling

Background: Neonatal mortality remains a major public health concern in sub-Saharan Africa. Evidence from facility-based studies may be affected by selection and referral biases. This study examined the association between short birth intervals, maternal care, and other maternal, reproductive, and healthcare factors at different causal levels and neonatal mortality in a referral hospital in Rwanda, using a hierarchical modelling approach. Methods: A retrospective cohort study was conducted among 1042 mother–neonate pairs admitted between 2022 and 2023. Variables were grouped into distal (socioeconomic), intermediate (reproductive), and proximal (clinical) levels. Sequential logistic regression models were applied. Due to substantial non-random missingness in birth interval data, complete-case analysis was used for intermediate model, with sensitivity analyses to assess robustness. A Directed Acyclic Graph was used to evaluate potential biases. Results: Among 1,042 neonates, 32 (3.07%) died. Lower maternal education was associated with higher odds of neonatal mortality (aOR 2.18; 95% CI 1.08– 4.54), although this may reflect selection bias. Short birth interval showed an inverse association with neonatal mortality, but this finding was unstable and not interpretable due to non-random missing data and bias. Proximal factors showed consistent associations: inadequate antenatal care (< 4 visits) was associated with higher odds (aOR 3.12; 95% CI 1.29– 7.54), and preterm birth was strongly associated (aOR 11.67; 95% CI 3.74– 36.35). Conclusion: Proximal clinical factors, particularly preterm birth and inadequate antenatal care, were consistently associated with neonatal mortality. Associations with distal and intermediate factors were affected by bias and missing data, limiting their interpretability. These findings underscore the need for caution when interpreting hospital-based data and highlight the value of hierarchical modelling with causal frameworks. Plain Language Summary: Deaths during the first month of life remain a major problem in sub-Saharan Africa, including Rwanda. Although many women attend antenatal care, some newborns still die. Understanding why this happens can help improve care. This study used hospital records from a referral hospital in Kigali to examine factors linked to newborn deaths. Instead of analysing all factors at once, we grouped them into three levels. First, we looked at social and reproductive factors such as education and number of previous pregnancies. Second, we examined birth spacing. Third, we assessed medical and healthcare-related factors, including antenatal care attendance and whether the baby was born too early. We found that medical factors were most strongly linked to newborn deaths. Babies born too early and those whose mothers attended fewer antenatal care visits had a much higher risk of dying. Some other findings were unexpected. For example, shorter birth intervals appeared to be linked to lower risk. However, this is likely due to the type of data used. Because this study was based on hospital admissions, it may not include babies who died before reaching care. This can make some factors appear protective when they are not. These results show that improving the quality of antenatal care and the management of preterm births is important for reducing newborn deaths. They also highlight that hospital-based data should be interpreted carefully, as they may not reflect what is happening in the wider population.